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          <p>​    最近在调试MM32F103CBT6的低功耗，记录了几个降低电流的点。</p>
<p>1.把PB10和PA9引脚设置成模拟输入，进stop模式后测试电流可以降低。但是这两个引脚原来已经接地了，按理说不需要特别处理，比较奇怪，先记录一下。</p>
<p>2.程序开始初始化了SPI引脚，在进stop模式前把spi的引脚都设置成模拟输入，等唤醒后再把那几个spi引脚重新配置成spi的功能，进stop模式后测试电流可以降低。</p>
<p><img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20211116202254.png"></p>
<ol start="3">
<li>PWR_EnterSTOPMode(PWR_Regulator_ON, PWR_STOPEntry_WFI);                // 进入stop模式</li>
</ol>
<p>​       PWR_EnterSTOPMode(PWR_Regulator_LowPower, PWR_STOPEntry_WFI);   //进standby模式</p>
<p><img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/%E5%BE%AE%E4%BF%A1%E5%9B%BE%E7%89%87_20211116203313.png"></p>
<ol start="4">
<li>测试了MM32F103CBT6在进stop模式后，电流大概在0.43ma左右，和官方给的手册基本吻合。</li>
</ol>
<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/微信截图_20211116203528.png" style="zoom:80%;">


      
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          <h3 id="递归型一次对消器"><a href="#递归型一次对消器" class="headerlink" title="递归型一次对消器"></a>递归型一次对消器</h3><img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/202111152115692.png" style="zoom:60%;">

<p>其中或后面的那个式子的推导如下图所示：</p>
<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/202111152118964.png" style="zoom:80%;">

<p>​        其中k值大小取的不一样，效果也不一样，这个可以通过matlab来仿真。其中matlab仿真代码中主要用到freqz函数，这个函数这里就不介绍了。</p>
<h3 id="仿真代码"><a href="#仿真代码" class="headerlink" title="仿真代码"></a>仿真代码</h3><figure class="highlight matlab"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br></pre></td><td class="code"><pre><span class="line">clc;clear all;close all;</span><br><span class="line"></span><br><span class="line">f  = <span class="number">0</span>:<span class="number">0.01</span>:<span class="number">1</span>;</span><br><span class="line">FS = <span class="number">1</span>;</span><br><span class="line"></span><br><span class="line">k = <span class="number">0.25</span>;</span><br><span class="line">b = [<span class="number">1</span> <span class="number">-1</span>];</span><br><span class="line">a = [<span class="number">1</span> -k];</span><br><span class="line">[H,W] = freqz(b,a,f,FS);</span><br><span class="line">y1 = <span class="built_in">abs</span>(H);</span><br><span class="line"></span><br><span class="line">k = <span class="number">0.7</span>;</span><br><span class="line">b = [<span class="number">1</span> <span class="number">-1</span>];</span><br><span class="line">a = [<span class="number">1</span> -k];</span><br><span class="line">[H,W] = freqz(b,a,f,FS);</span><br><span class="line">y2 = <span class="built_in">abs</span>(H);</span><br><span class="line"></span><br><span class="line">k = <span class="number">0.9</span>;</span><br><span class="line">b = [<span class="number">1</span> <span class="number">-1</span>];</span><br><span class="line">a = [<span class="number">1</span> -k];</span><br><span class="line">[H,W] = freqz(b,a,f,FS);</span><br><span class="line">y3 = <span class="built_in">abs</span>(H);</span><br><span class="line"></span><br><span class="line">k = <span class="number">0.98</span>;</span><br><span class="line">b = [<span class="number">1</span> <span class="number">-1</span>];</span><br><span class="line">a = [<span class="number">1</span> -k];</span><br><span class="line">[H,W] = freqz(b,a,f,FS);</span><br><span class="line">y4 = <span class="built_in">abs</span>(H);</span><br><span class="line"></span><br><span class="line"><span class="built_in">figure</span>;</span><br><span class="line"><span class="built_in">hold</span> on;</span><br><span class="line"><span class="built_in">plot</span>(f,y1,<span class="string">&#x27;r-&#x27;</span>);</span><br><span class="line"><span class="built_in">plot</span>(f,y2,<span class="string">&#x27;b*&#x27;</span>);</span><br><span class="line"><span class="built_in">plot</span>(f,y3,<span class="string">&#x27;k^&#x27;</span>);</span><br><span class="line"><span class="built_in">plot</span>(f,y4,<span class="string">&#x27;r*&#x27;</span>);</span><br><span class="line"><span class="built_in">hold</span> off;</span><br><span class="line"><span class="built_in">legend</span>(<span class="string">&#x27;k=0.25&#x27;</span>,<span class="string">&#x27;k=0.7&#x27;</span>,<span class="string">&#x27;k=0.9&#x27;</span>,<span class="string">&#x27;k=0.98&#x27;</span>)</span><br></pre></td></tr></table></figure>
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          <p>​        用来抑制固定地物杂波的滤波器叫做对消器，它利用固定地物的多普勒频率为0的特点，采用跨周期相消方式抑制掉回波中的固定地物杂波。</p>
<p>​        下面这个图可以比较直观的显示对消器的作用：</p>
<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/202111152050091.png" style="zoom:80%;">

<h3 id="非递归型一次对消器"><a href="#非递归型一次对消器" class="headerlink" title="非递归型一次对消器"></a>非递归型一次对消器</h3><p>​        非递归型一次对消器简称一次对消器，也称两脉冲对消器，其具有消除或减小雷达相干检波器输出信号中杂波的功能，结构图如下所示：</p>
<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/202111152054596.png" style="zoom:59%;">

<p>​        在有的书籍中，上面这个也叫单延迟线对消器：</p>
<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/202111152057234.png" style="zoom:80%;">

<h3 id="存在的问题："><a href="#存在的问题：" class="headerlink" title="存在的问题："></a>存在的问题：</h3><p>​        一次固定对消器的幅频特性是半余弦状，不是理想的矩形，又因为杂波内部相对运动、天线扫描、A/D变换器位数等因素的影响，固定一次对消器的改善因子不可能很高，在对付强地物杂波时，常采用两个一次对消器级联的办法提高对消器的改善因子。</p>
<h3 id="单延迟线对消器-双延迟线对消器仿真："><a href="#单延迟线对消器-双延迟线对消器仿真：" class="headerlink" title="单延迟线对消器/双延迟线对消器仿真："></a>单延迟线对消器/双延迟线对消器仿真：</h3><figure class="highlight matlab"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br></pre></td><td class="code"><pre><span class="line">clc;clear all;close all;</span><br><span class="line"></span><br><span class="line">f  = <span class="number">0</span>:<span class="number">0.01</span>:<span class="number">3</span>;</span><br><span class="line">FS = <span class="number">1</span>;</span><br><span class="line"></span><br><span class="line">b  = [<span class="number">1</span> <span class="number">-1</span>];</span><br><span class="line">a  = [<span class="number">1</span>];</span><br><span class="line">[H,w]=freqz(b,a,f,FS);</span><br><span class="line">HF_0=<span class="built_in">abs</span>(H)/<span class="built_in">max</span>(<span class="built_in">abs</span>(H));</span><br><span class="line"></span><br><span class="line">b  = conv(b,b);			<span class="comment">%级联</span></span><br><span class="line">[H,w]=freqz(b,a,f,FS);</span><br><span class="line">HF_1=<span class="built_in">abs</span>(H)/<span class="built_in">max</span>(<span class="built_in">abs</span>(H));</span><br><span class="line"><span class="built_in">figure</span>;subplot(<span class="number">2</span>,<span class="number">1</span>,<span class="number">1</span>);</span><br><span class="line"><span class="built_in">hold</span> on;</span><br><span class="line"><span class="built_in">plot</span>(w,HF_0);             </span><br><span class="line"><span class="built_in">plot</span>(w,HF_1);</span><br><span class="line"><span class="built_in">hold</span> off;</span><br><span class="line"><span class="built_in">legend</span>(<span class="string">&#x27;单延迟线&#x27;</span>,<span class="string">&#x27;双延迟线&#x27;</span>)</span><br><span class="line"></span><br><span class="line">subplot(<span class="number">2</span>,<span class="number">1</span>,<span class="number">2</span>);</span><br><span class="line"><span class="built_in">hold</span> on;</span><br><span class="line"><span class="built_in">plot</span>(w,<span class="number">20</span>*<span class="built_in">log10</span>(HF_0));             </span><br><span class="line"><span class="built_in">plot</span>(w,<span class="number">20</span>*<span class="built_in">log10</span>(HF_1));</span><br><span class="line"><span class="built_in">hold</span> off;</span><br><span class="line"><span class="built_in">legend</span>(<span class="string">&#x27;单延迟线&#x27;</span>,<span class="string">&#x27;双延迟线&#x27;</span>)</span><br></pre></td></tr></table></figure>
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          <h3 id="背景"><a href="#背景" class="headerlink" title="背景"></a>背景</h3><p>​        之前在公司电脑上搭建了Hexo博客，后来自己电脑上也需要搭建一个，这自然就涉及到在多台电脑上维护hexo博客。</p>
<h3 id="方法"><a href="#方法" class="headerlink" title="方法"></a>方法</h3><p>​        使用git分支，这个方法来自于网友，原文链接为：<a target="_blank" rel="noopener" href="http://linyk.me/2017/03/24/hexo-github-pages/">http://linyk.me/2017/03/24/hexo-github-pages/</a></p>
<p>​        用个人博客仓库的master分支来保存博客静态页面，新建另一个分支来保存hexo部署环境目录！之后每次博客的更新就在新分支上编辑和发布（deploy），把生成的博客页面推到master分支上。</p>
<h3 id="一、准备工作"><a href="#一、准备工作" class="headerlink" title="一、准备工作"></a>一、准备工作</h3><p>​        公司那台电脑已经使用hexo在gitee pages上面部署好了自己的博客，并在电脑上有该博客的部署环境，即类似下面这个图中的文件目录：</p>
<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/202111072008482.png" style="zoom:70%;">

<h3 id="二、对自己推送的博客仓库新建分支，并克隆"><a href="#二、对自己推送的博客仓库新建分支，并克隆" class="headerlink" title="二、对自己推送的博客仓库新建分支，并克隆"></a>二、对自己推送的博客仓库新建分支，并克隆</h3><p>​        在自己的博客仓库上新建一个develop分支，名字也可以取其他的，并切换到这个分支，并在该仓库中将默认分支设为develop。</p>
<p><img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/202111072013876.png"></p>
<p>并将这个仓库克隆到本地，进入该仓库文件目录。用git branch命令查看当前分支，应在新建的develop分支。</p>
<h3 id="三、本地博客的部署文件拷贝到步骤二仓库文件目录里"><a href="#三、本地博客的部署文件拷贝到步骤二仓库文件目录里" class="headerlink" title="三、本地博客的部署文件拷贝到步骤二仓库文件目录里"></a>三、本地博客的部署文件拷贝到步骤二仓库文件目录里</h3><p>​        将本地博客的部署文件都拷贝到步骤二仓库文件目录里：</p>
<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/202111072018494.png" style="zoom:80%;">

<p>​    接下来就是将拷贝进来的博客hexo部署环境提交到develop分支，提交前注意：将Hexo目录下的.git目录删掉，因为一个git仓库中不能包含另一个git仓库。</p>
<h3 id="四、提交develop分支"><a href="#四、提交develop分支" class="headerlink" title="四、提交develop分支"></a>四、提交develop分支</h3><p>​        按下面三步就可以将博客的hexo部署环境提交到仓库的develop分支。</p>
<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/202111072028164.png" style="zoom:80%;">

<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/202111072028921.png" style="zoom:80%;">

<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/202111072028415.png" style="zoom:80%;">

<p>​        master分支和develop分支各自保存着一个版本，master分支用于保存博客静态资源，提供博客页面供人访问；develop分支用于备份博客部署文件，供自己维护更新，两者在一个gitee仓库内互不冲突，完美！</p>
<h3 id="五、新电脑环境部署和更新"><a href="#五、新电脑环境部署和更新" class="headerlink" title="五、新电脑环境部署和更新"></a>五、新电脑环境部署和更新</h3><p>​        在新电脑上克隆步骤二中的仓库的develop分支到本地，此时本地git仓库处于develop分支。</p>
<p>​        切换到Hexo目录，执行npm install(由于在旧电脑上提交Hexo的时候，里面有.gitignore文件，里面默认忽略node_modules文件夹的)，执行完之后，在Hexo目录下就可以看到node_modules文件夹了。</p>
<p>​        之后你就可以在新电脑上添加博文了，依次执行git add .    git commit -m “xxx” ， git push origin develop，保证develop分支版本最新，这个也相当于把博文在gitee仓库里备份了，再也不怕本地文件丢失了。</p>
<p>​        对于要部署到网上的博文，进入Hexo目录，按照之前的指令hexo clean，hexo g，hexo d就可以，没什么不一样的地方，完成后就会发现，已经更新到master分支了，两个分支互不干扰！</p>
<p><strong>注意：</strong></p>
<p>​        每次换电脑进行博客更新时，不管上次在其他电脑有没有更新（就怕更新之后忘了），最好先git pull获取develop分支的最新版本，之后再进行编辑和提交。</p>
<h3 id="六、文章更新时间变成同一天问题"><a href="#六、文章更新时间变成同一天问题" class="headerlink" title="六、文章更新时间变成同一天问题"></a>六、文章更新时间变成同一天问题</h3><p>​        在新电脑上部署提交后，网页上所有的文章都显示同一天，并且进入归档看到归档的文章也都显示的同一天，这样我就不知道我在不同年月写的不同博文了。解决方法如下：</p>
<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/202111072047559.png" style="zoom:80%;">

<p>添加了如上两行，这样网页上显示的发布于就正确了。</p>
<p>​        对于归档显示同一天的问题，解决方法如下：</p>
<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/202111072050121.png" style="zoom:80%;">
      
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          <p>​    在使用SSH协议访问Git仓库前，需要先配置好账户—仓库的ssh公钥。</p>
<p>1.命令行中输入命令，填写自己的邮箱，并输入三次回车</p>
<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/202111062336896.png" style="zoom:80%;">

<p>2.去C盘中找到相应文件，里面的内容即为生成的SSH公钥。</p>
<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/202111062337278.png" style="zoom:80%;">

<p>3.在码云 Gitee的设置-SSH公钥中，添加公钥</p>
<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/202111062340637.png" style="zoom:80%;">

<p>4.这样就都搞定了，可以通过git下载gitee仓库中的代码了。</p>

      
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          <p>​        下面理论部分的来自于csdn上的一篇文章，原文地址：<a target="_blank" rel="noopener" href="https://blog.csdn.net/zhoupian/article/details/90256065">https://blog.csdn.net/zhoupian/article/details/90256065</a> 感谢原作者。</p>
<h3 id="简介"><a href="#简介" class="headerlink" title="简介"></a><strong>简介</strong></h3><p>​        alpha-beta滤波（ αβ滤波）是一种可用于状态估计、数据平滑的滤波器，如果对kalman滤波有了解，就会发现 αβ滤波的形式和kalman相近，但是其最突出的优点是它不依赖系统的具体模型，因此使用起来更简单。</p>
<h3 id="滤波方程"><a href="#滤波方程" class="headerlink" title="滤波方程"></a><strong>滤波方程</strong></h3><p>​        假设一个二阶状态模型，比如车辆的速度v、位置p，观测量（测量值）与一阶对应，即位置p。如果滤波周期△T比较小，假设速度v这段时间内保持恒定（加速度为或者变化缓慢），与kalman滤波的一步预测类似，我们可以通过如下方程预测下个时刻系统的位置：</p>
<p>(1)</p>
<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/微信截图_20211104155153.png" style="zoom:67%;">

<p>​        因为假设速度是恒定值，下个时刻的速度与上一个时刻相同。</p>
<p>(2)</p>
<p>​        <img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/微信截图_20211104155326.png" style="zoom:67%;"></p>
<p>​        由于方程中并没有考虑噪声和系统的其它动态特性，因此实际的测量值会与上面的预测值不一样，即两者之间存在误差r，我们称r为残差或新息。</p>
<p>(3)</p>
<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/微信截图_20211104155503.png" style="zoom:67%;">

<p>​        αβ滤波采用选定的α和β常数（滤波器得到其名称），使用偏差r的α倍来校正位置估计，并使用β乘以偏差r来校正速度估计，使用ΔT来归一化β的大小。</p>
<p>(4)</p>
<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/微信截图_20211104155716.png" style="zoom:67%;">

<p>​        可以将上述的校正视为沿着梯度下降方向的一小步，随着不断的调整，误差逐渐减小或消除被消除。</p>
<p>​        为了保证系统的稳定性，α和β的值必须是一个正值且很小，一般情况下需要满足下式：</p>
<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/微信截图_20211104155825.png" style="zoom:67%;">

<h3 id="代码实例"><a href="#代码实例" class="headerlink" title="代码实例"></a>代码实例</h3><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> matplotlib.pyplot <span class="keyword">as</span> plt</span><br><span class="line"></span><br><span class="line">ini_pos = <span class="number">10</span>          <span class="comment"># 初始位置10m</span></span><br><span class="line">move_v = <span class="number">2</span>            <span class="comment"># 初始运动速度2m/s</span></span><br><span class="line">T = <span class="number">0.1</span>               <span class="comment"># 采样间隔0.1s</span></span><br><span class="line">observe_time = <span class="number">50</span>     <span class="comment"># 观测时间50s</span></span><br><span class="line"></span><br><span class="line">alpha = <span class="number">0.1</span></span><br><span class="line">beta = <span class="number">0.1</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">if</span> __name__ == <span class="string">&#x27;__main__&#x27;</span>:</span><br><span class="line">    x_prd = np.zeros(<span class="built_in">int</span>(observe_time / T))   <span class="comment"># 距离预测值</span></span><br><span class="line">    v_prd = np.zeros(<span class="built_in">int</span>(observe_time/T))     <span class="comment"># 速度预测值</span></span><br><span class="line">    observe = np.zeros(<span class="built_in">int</span>(observe_time/T))   <span class="comment"># 观测值</span></span><br><span class="line">    residual = np.zeros(<span class="built_in">int</span>(observe_time/T))  <span class="comment"># 观测值与预测值的差</span></span><br><span class="line">    x_real = np.zeros(<span class="built_in">int</span>(observe_time/T))    <span class="comment"># 真实值</span></span><br><span class="line"></span><br><span class="line">    x_real[<span class="number">0</span>] = ini_pos</span><br><span class="line">    observe[<span class="number">0</span>] = x_real[<span class="number">0</span>] + np.random.normal(loc=<span class="number">0</span>, scale=<span class="number">3</span>)  <span class="comment"># 加高斯随机分布的随机数</span></span><br><span class="line">    residual[<span class="number">0</span>] = observe[<span class="number">0</span>] - x_prd[<span class="number">0</span>]</span><br><span class="line"></span><br><span class="line">    <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">1</span>, <span class="built_in">int</span>(observe_time/T)):</span><br><span class="line">        <span class="comment"># prediction</span></span><br><span class="line">        x_prd[i] = x_prd[i-<span class="number">1</span>] + T*v_prd[i-<span class="number">1</span>]</span><br><span class="line">        v_prd[i] = v_prd[i-<span class="number">1</span>]</span><br><span class="line">        <span class="comment"># measured</span></span><br><span class="line">        x_real[i] = x_real[i-<span class="number">1</span>] + move_v*T</span><br><span class="line">        observe[i] = x_real[i] + np.random.normal(loc=<span class="number">0</span>, scale=<span class="number">3</span>)</span><br><span class="line">        <span class="comment"># residual</span></span><br><span class="line">        residual[i] = observe[i] - x_prd[i]</span><br><span class="line">        <span class="comment"># update</span></span><br><span class="line">        x_prd[i] = x_prd[i] + alpha * residual[i]</span><br><span class="line">        v_prd[i] = v_prd[i] + beta * residual[i]/T</span><br><span class="line"></span><br><span class="line">    t = np.arange(<span class="number">0</span>, observe_time, T)</span><br><span class="line"></span><br><span class="line">    plt.clf()</span><br><span class="line">    plt.subplot(<span class="number">111</span>)</span><br><span class="line">    plt.title(<span class="string">&#x27;position&#x27;</span>)</span><br><span class="line">    plt.ylabel(<span class="string">&#x27;position[m]&#x27;</span>)</span><br><span class="line">    plt.xlabel(<span class="string">&#x27;Time[s]&#x27;</span>)</span><br><span class="line">    plt.xlim(<span class="number">0</span>, observe_time)</span><br><span class="line">    plt.plot(t, x_real, <span class="string">&#x27;r--&#x27;</span>, t, observe, <span class="string">&#x27;bs&#x27;</span>, t, x_prd, <span class="string">&#x27;g^&#x27;</span>)</span><br><span class="line">    plt.show()</span><br><span class="line"></span><br></pre></td></tr></table></figure>
<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/F1igure_1.png" style="zoom:67%;">

<p>​        α和β越小，噪声越小，滤波后的值更平滑，但是动态性能差,这里把α和β的值都改成0.01，效果如下图：</p>
<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/Figure_1222.png" style="zoom:67%;">

<p>​        α和β越大，滤波具有更快的动态性能，但是噪声也会加大,这里把α和β的值都改成0.2,效果如下图：</p>
<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/Figewewure_1.png" style="zoom:67%;">
      
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          <p><strong>问题：</strong></p>
<p>​        调试之前的工程，唯一不同的地方是把usb fs改成了usb hs，stm32的工程，用的cubemx生成的usb代码，数据发送和接收简单试了一下都没问题。结果整个程序跑起来之后出现bug，和预期不一样。</p>
<p><strong>原因：</strong></p>
<p>​        出问题的地方在CDC_Receive_HS这个函数，之前用的是CDC_Receive_FS。在这函数里面我之前有个数组，大小设置成了256，错就错在了这个地方。。。。。</p>
<p>​        <strong>默认定义下USB_FS收发每一个包的大小均为64字节</strong>，如果有一包数据很大，它会每次收到64字节。现在我用了usb hs，<strong>默认定义下USB_HS收发每一个包的大小均为512字节。</strong>当我收比如260字节的时候，CDC_Receive_HS不再是64字节，64字节……这样收了，而是一下子把260字节都收到，这样之前我的那个数组大小就不够用了。</p>
<p>​        后来把那个数组大小放大，解决问题！！</p>

      
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          <p>​        之前调试一个代码，usb那边收数据，收到数据后把数据放到queue里面，结果程序调用xQueueSendFromISR就卡死在里面port.c的configASSERT。</p>
<img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/微信截图_20211026173949.png" style="zoom:77%;">

<p>​        原因：usb的中断等级太高了，降低后就可以解决了。</p>
<p>​        使用FreeRTOS API的中断不能保留其默认优先级为0，如果一个服务例程(ISR)为一个优先级高于configMAX_SYSCALL_INTERRUPT_PRIORITY的中断调用一个ISR安全的FreeRTOS API函数，下面的断言将失败。ISR安全的FreeRTOS API函数必须仅从分配了优先级在configMAX_SYSCALL_INTERRUPT_PRIORITY或以下的中断中调用。数字上的低中断优先级数字代表逻辑上的高中断优先级，因此中断的优先级必须设置为等于或数字上高于的值configMAX_SYSCALL_INTERRUPT_PRIORITY。</p>
<p>​        使用FreeRTOS API的中断不能保留其默认优先级为0，因为这是可能的最高优先级，它保证高于configMAX_SYSCALL_INTERRUPT_PRIORITY，因此也保证无效。FreeRTOS维护独立的线程和ISR API函数，以确保中断入口尽可能快速和简单。</p>

      
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          <p>​        原文地址：<a target="_blank" rel="noopener" href="https://blog.csdn.net/weixin_45877759/article/details/107141789">https://blog.csdn.net/weixin_45877759/article/details/107141789</a></p>
<p>1.在博客的本地根目录hexo下面执行：npm install hexo-generator-searchdb –save</p>
<p>2.修改hexo的配置文件（位置：根目录/_config.yml）</p>
<p>在这个文件的最后添加：</p>
<figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre></td><td class="code"><pre><span class="line"># Search Config</span><br><span class="line">search:</span><br><span class="line">  path: search.json #之前就是这里出了问题导致图标错误，错误的教程是search.xml</span><br><span class="line">  field: post</span><br><span class="line">  format: html</span><br><span class="line">  limit: 1000</span><br></pre></td></tr></table></figure>
<p>3.进入主题的根目录，修改配置(Hexo\themes\next \_config.yml)</p>
<p>​    将local_search那边的enable改成true.</p>
<p>​    这样就可以了。</p>

      
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          <p>​        在ubuntu平台上使用arm-none-eabi-gcc交叉编译工具把原来stm32上的一个代码编译成静态库，在这个过程中遇到了两个问题，这边记录一下。</p>
<p>1.arm-none-eabi-gcc: no such file or directory</p>
<p>​        发现是系统默认不支持以前的32位版本的开发包，可以使用如下命令，使其识别老版本的开发包，命令如下：apt-get install gcc-multilib</p>
<p>2.执行cmake的时候报：is not able to compile a simple test program</p>
<p><img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20210915213300.png"></p>
<p>​        查找了网上的资料，解决方法如下：</p>
<p><img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20210915222937.png"></p>
<p>我这边修改了toolchain文件，添加了那条语句，确实就可以了。</p>
<p><img src="https://xdl-blog-picture.oss-cn-shanghai.aliyuncs.com/img/%E5%BE%AE%E4%BF%A1%E6%88%AA%E5%9B%BE_20210915223938.png"></p>

      
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